1. Field
The present invention relates to an apparatus and method for detecting an object. In particular, the present invention relates to an apparatus and method for detecting a human component and an apparatus and method for detecting an object using a difference image as a feature image.
2. Description of the Related Art
Object detection is very important in video analysis technology, such as content-based video or image object retrieval, video surveillance, object recognition, frequency control, video compression and automatic driver-assistance. In the real world, a human being is one of the most challenging categories for detection. Human detection can be divided into three types of cases.
The first case is when an object detection system determines whether there is a man in a field. For example, in the case the object detection system is for driving assistance, when a pedestrian on a road is near a vehicle, the system generates an alarm to alert a driver. This may be implemented as an embedded smart device integrated with an imaging apparatus.
The second case is coherent tracking of an individual person in a static camera or a PTZ camera. The static camera may collect moving trajectories of the person, and apply the same to intelligent behavior analysis and the PTZ camera may adjust its position to track and center the person in an image and record detail or behaviors.
The third case is when a robot performs tracking of a person, observing the person, or interacting with the person. The robot will attempt to detect the person's position in the image of its camera, and perform corresponding actions, such as moving, tracking, observing, or adjusting its position, which can be implemented by an embedded device integrated into a functional apparatus of the robot.
Because a partial appearance and overall shape varies due to various fashion shapes and styles, it is relatively difficult to find a feature included in a category. Thus, a feature population having stability and capable of being identified even under poor surroundings to extract a feature, such as poor lightning and a complex background, is needed. In addition, the overall shape varies due to accuracy and many occluding accessories and silhouettes are modified due to many people present in the same image region. Thus, an algorithm is needed through which a small amount of interference from an overall recorded image may be overcome and accurate result may be drawn.
A conventional human detection method uses either a global model such as full-body appearance or silhouettes, or a local feature population or a component detecting apparatus. The first method extracts features of full-body human and builds the global models based on its appearance or silhouettes. The second method segments a human body into several components, such as a human head, human torso, human legs and arms. Human detection is seen as component detection, and research subjects are focused on simplifying a corresponding human component model. Pattern learning methods generally include schemes such as support vector machine (SVM), Adaboost, and the like.
As is well known, face detection has made significant progress in the past few years, and achieves high detection rates and low false alarm rates in real-time processing. However, human detection still requires many advances to be realized for application in the real-world. First, a human detecting apparatus should be able to adapt itself to changes of human appearance due to different clothing style and different illumination conditions. Second, the human detecting apparatus should be built on robust features which capture characteristic patterns of humans from their various deformations. Finally, the human detecting apparatus should require relatively fewer computations and real-time processing capability.